Application of wideband sampling for arc detection with a probabilistic model for quantitatively measuring arc events

ABSTRACT

An arc detection system for a plasma generation system includes a radio frequency (RF) sensor that generates first and second signals based on a respective electrical properties of (RF) power that is in communication with a plasma chamber. A correlation module generates an arc detect signal based on the first and second signals. The arc detect signal indicates whether an arc is occurring in the plasma chamber and is employed to vary an aspect of the RF power to extinguish the arc.

FIELD

The present disclosure relates to detecting arcs in a radio frequency(RF) plasma generation system.

BACKGROUND

The background description provided herein is for the purpose ofgenerally presenting the context of the disclosure. Work of thepresently named inventors, to the extent it is described in thisbackground section, as well as aspects of the description that may nototherwise qualify as prior art at the time of filing, are neitherexpressly nor impliedly admitted as prior art against the presentdisclosure.

Plasma chambers can be used for performing various processes such aschemical vapor deposition, sputter deposition and plasma-enhancedetching processes used in manufacturing an electronic work piece such asa semiconductor device or flat panel display. A plasma discharge issustained by coupling RF or DC power from an electrical power source tothe plasma. The coupling is accomplished typically by connecting thepower source to an electrode within the chamber or to an antenna ormagnetic coil within or adjacent to the chamber.

The conditions within a plasma chamber generally change during theprogression of the manufacturing process being performed within thechamber, and such changes sometimes cause electrical arcing within thechamber. If any electrical arcing occurs between the plasma and the workpiece being manufactured, or between the plasma any of the chambercomponents, damage may occur to the work piece or the chambercomponents.

SUMMARY

An arc detection system for a plasma generation system includes a radiofrequency (RF) sensor that generates first and second signals based onrespective electrical properties of (RF) power that is in communicationwith a plasma chamber. A correlation module generates an arc detectsignal based on the first and second signals. The arc detect signalindicates whether an arc is occurring in the plasma chamber and isemployed to vary an aspect of the RF power to extinguish the arc.

In other features, a subtraction module subtracts signal levels fromrespective ones of the first and second signals. A window module appliesa window function to the first and second signals. A probabilisticmodule computes a probability of an arc event based on the arc detectsignal. The probabilistic module employs a Baum-Welch algorithm tocalculate a probabilistic model of the arc event. The probabilisticmodule employs a Viterbi algorithm to compute the probability of the arcevent. The correlation module receives an enable signal that selectivelyenables generating the arc detect signal. An analog-to-digital (A/D)conversion module digitizes the first and second signals. The RF sensorcan be a voltage/current (V/I) sensor wherein the first and secondsignals represent a voltage and current, respectively, of the RF power.The RF sensor can be a directional coupler wherein the first and secondsignals represent the forward power and reflected power, respectively,of the RF power.

An arc detection method for a plasma generation system includesgenerating first and second signals based on respective electricalproperties of (RF) power that is in communication with a plasma chamberand generating an arc detect signal based on the first and secondsignals. The arc detect signal indicates whether an arc is occurring inthe plasma chamber. The method includes employing the arc detect signalto vary an aspect of the RF power to extinguish the arc.

In other features the method includes subtracting signal levels fromrespective ones of the first and second signals. The method includesselecting periods of the first and second signals for communicating tothe correlation module. The method includes computing a probability ofan arc event based on the arc detect signal. The computing step furthercomprises employing a Baum-Welch algorithm to calculate a probabilisticmodel of the arc event. The computing step further comprises employing aViterbi algorithm to compute the probability of the arc event. Themethod includes receiving an enable signal that selectively enablesgenerating the arc detect signal. The method includes digitizing thefirst and second signals.

An arc detection system for a plasma generation system includes a radiofrequency (RF) sensor that generates first and second signals based on arespective electric properties RF power that is in communication with aplasma chamber. An analog-to-digital (A/D) conversion module generatesdigital data based on the first and second signals. A subtraction modulesubtracts values from the digital data. A window module applies a windowfunction to the digital data. A correlation module correlates the firstand second signals as they are represented in the windowed digital dataand generates an arc detect signal based on the correlation. The arcdetect signal indicates whether an arc is occurring in the plasmachamber.

In other features, a probabilistic module computes a probability of anarc event based on the arc detect signal. The probabilistic moduleemploys a Baum-Welch algorithm to calculate a probabilistic model of thearc event. The probabilistic module employs a Viterbi algorithm tocompute the probability of the arc event. The correlation modulereceives an enable signal that selectively enables generating the arcdetect signal. The RF sensor can be a voltage/current (V/I) sensorwherein the first and second signals represent a voltage and a current,respectively, of the RF power. The RF sensor can be a directionalcoupler wherein the first and second signals represent a forward powerand a reflected power, respectively, of the RF power.

Further areas of applicability of the present disclosure will becomeapparent from the detailed description provided hereinafter. It shouldbe understood that the detailed description and specific examples areintended for purposes of illustration only and are not intended to limitthe scope of the disclosure.

DRAWINGS

The drawings described herein are for illustration purposes only and arenot intended to limit the scope of the present disclosure in any way.

FIG. 1 is a functional block diagram of a radio frequency (RF) plasmageneration system;

FIG. 2 is a functional block diagram of an analysis module;

FIGS. 3A and 3B are respective waveforms of normalized RF voltage andcurrent signals;

FIG. 4 is a graph of an autocorrelation function of the RF voltagesignal of FIG. 3A;

FIG. 5 is a graph of an autocorrelation function of the RF currentsignal of FIG. 3B;

FIG. 6 is a graph of cross correlation of the RF voltage and currentsignals of FIGS. 3A and 3B;

FIG. 7 is a graph of an autocorrelation function of the RF voltagesignal for k:=4;

FIG. 8 is a graph of an autocorrelation function of the RF currentsignal for k:=4;

FIG. 9 is a graph of a first difference of the autocorrelation functionfor the voltage signal of FIG. 3A;

FIG. 10 is a graph of a first difference of the autocorrelation functionfor the current signal of FIG. 3B

FIG. 11 is a graph of normalized output for cross correlation of thevoltage and current signals;

FIGS. 12A and 12B are respective waveforms of a normalized voltagesignal and normalized current signal of an arc having brief durationwhen compared to a sample interval;

FIG. 13 is a graph of the arc event of FIGS. 12A and 12B detected byblock processing the time difference of a VI cross correlation function,k:=4; and

FIG. 14 is a Markov chain describing the arc process.

DETAILED DESCRIPTION

The following description is merely exemplary in nature and is in no wayintended to limit the disclosure, its application, or uses. For purposesof clarity, the same reference numbers will be used in the drawings toidentify similar elements. As used herein, the phrase at least one of A,B, and C should be construed to mean a logical (A or B or C), using anon-exclusive logical or. It should be understood that steps within amethod may be executed in different order without altering theprinciples of the present disclosure.

As used herein, the term module refers to an Application SpecificIntegrated Circuit (ASIC), an electronic circuit, a processor (shared,dedicated, or group) and memory that execute one or more software orfirmware programs, a combinational logic circuit, and/or other suitablecomponents that provide the described functionality.

Referring now to FIG. 1, one of several embodiments is shown of a radiofrequency (RF) plasma generator system 10. RF plasma generator system 10includes an RF generator 12 that generates RF power for a plasma chamber18. An RF sensor 16 generates first and second signals that representrespective electrical properties of the RF power. RF sensor 16 may beimplemented with a voltage/current (V/I) sensor or a directionalcoupler. When RF sensor 16 is implemented with the V/I sensor the firstand second signals represent voltage and current of the RF power,respectively. When the RF sensor 16 is implemented with the directionalcoupler the first and second signals represent forward and reverse powerof the RF power, respectively. It should be appreciated that theremainder of this description assumes RF sensor 16 is implemented withthe V/I sensor, however the description also applies when RF sensor 16is implemented with the directional coupler. When using the directionalcoupler then forward and reverse power should replace references in thespecification to the voltage and current of the RF power.

An impedance matching network 14 matches an output impedance of RFgenerator 12 to an input impedance of plasma chamber 18. Impedancematching network 14 is shown connected downstream of RF sensor 16,however it should be appreciated that it may also be connected upstreamof RF sensor 16, i.e. between the RF sensor 16 and plasma chamber 18.

An analog to digital (A/D) module 20 converts the first and secondsignals from RF sensor 16 to respective digital signals. The digitalsignals are communicated to an analysis module 22. Analysis module 22employs a correlation function to detect arcs in plasma chamber 18 basedon the first and second signals. The arc detection method is describedbelow in more detail. Analysis module 22 generates an arc detect signalbased on an outcome of the arc detect method. The arc detect signal iscommunicated to a control module 24 and a probabilistic module 36 andindicates whether an arc is occurring in plasma chamber 18.

Control module 24 generates control signals 26 that control the RF poweroutput of RF generator 12. Control module 24 also receives the arcdetect signal and the data from the first and second signals viaanalysis module 22. Control module 24 generates an output based on thedata and the arc detect signal. The output controls RF generator 12 suchthat the plasma is generated as desired and any arc detected in theplasma is extinguished in response to the arc detect signal.

In some embodiments, RF generator 12 and/or control module 24 generatesan enable signal 28 and communicates it to analysis module 22. Enablesignal 28 is employed when RF generator 12 initiates plasma in plasmachamber 18. While the plasma is initiating, the voltage and current ofthe RF power fluctuate. Enable signal 28 holds off or disables analysismodule 22 such as to prevent it from misinterpreting the fluctuations asarcs.

In some embodiments, analysis module 22 may detect whether the plasma isinitiating and obviate a need for enable signal 28. Analysis module 22may determine whether plasma is initiating by monitoring the voltage andcurrent of the RF power. When the voltage and current transition fromzero to non-zero, then analysis module 22 may hold off generating thearc detect signal until after the voltage and current stabilize atnon-zero values.

A probabilistic module 36 may be employed to process the arc detectsignal in accordance with a method that is described below.Probabilistic module 36 uses the arc detect signal to compute aprobabilistic model and to predict a probability of an arc event. Themodel is computed using a Baum-Welch algorithm and the probability of anarc event is computed using a Viterbi algorithm. Probabilistic module 36may be an off-line process that generates the model after data iscollected. The resulting probabilistic model becomes a quantitativeindicator that determines whether variations to process parametersassociated with the semiconductor manufacturing process yield adecreased likelihood of arcs of various durations.

Referring now to FIG. 2, a functional block diagram is shown of analysismodule 22. Analysis module 22 includes a subtraction module 30, a windowmodule 32, and a correlation module 34. Subtraction module 30 subtractsa DC offset from the digital signals that are generated by A/D module20. Window module 32 applies a window function to the digital data fromsubtraction module 30. Correlation module 34 cross correlates thewindowed data in accordance with a method that is described below.

Operation of correlation module 34 will now be described in more detail.The wideband, high speed digital data from A/D module 20 providesvaluable information of the spectral content of the RF power present onthe RF transmission line between RF generator 12 and plasma chamber 18.Spatial information contained in these signals represents transitorybehavior of systems connected to the RF transmission line. Arc detectioncan be achieved by coupling the spatial information with computation ofa correlation function within correlation module 34. Probabilisticmodule 36 implements a probabilistic framework to bolster arc detectionand provide a quantitative measure to demonstrate process improvement bythe reduction of the likelihood of an arc event.

Arc events can be characterized by rapid and abrupt transients thatresult from a discharge between the RF generated plasma and an electrodeof plasma chamber 18. The arc events may damage devices being fabricatedduring a semiconductor manufacturing process. Other arc events arecharacterized by a discharge from the plasma to a sidewall of plasmachamber 18 and/or discharges within the plasma occurring from thebuild-up of polymer structures within the plasma. The polymerization ofnegative ions may also be referred to as dust particles. A sheath of theplasma for continuously powered plasma retains negative ions. After aperiod of time, these negative ions build up and polymerize to formcontaminating particles. When any of these arc events occur, thetransient resulting from the discharge causes perturbations on theelectromagnetic signals represented by the information from A/D module20.

Correlation module 34 implements a discrete-time auto correlationfunction

$\begin{matrix}{{r_{xx}(\tau)} = {\sum\limits_{\forall n}{{x\lbrack n\rbrack}{{x\left\lbrack {n - \tau} \right\rbrack}.}}}} & (1)\end{matrix}$

where x represents one of the first and second digital signals;

n is an index of the digital sample; and

τ is a lag or delay in the function.

Eq. (1) is an even function and its maximum value occurs at τ=0. Thisaids in an efficient implementation of an arc detection scheme describedbelow. Two additional properties of Eq. (1) are leveraged for thepurpose of arc detection. The first property is that Eq. (1) contains ameasure of the rate of change of the voltage and current. The secondproperty is the function is periodic if the voltage and current signalscontain periodic components. The correlation is performed on a windowedversion of the digital signal containing N discrete time samplescomprising M periods of the fundamental RF signal. Window module 32applies the windowing function to the digital samples.

The frequency of the RF power is referred to as the fundamental signal.In the event plasma generator system 10 has multiple RF generators 12with different operating frequencies; the fundamental signal is selectedas the lowest frequency in the lowest frequency band of operation.

The procedure to compute a spectral estimation of the signal commenceswith subtraction module 30 subtracting a mean μ_(x) from thediscrete-time signal x from A/D module 20.

$\begin{matrix}{\mu_{x} = {{E\lbrack x\rbrack} = {\frac{1}{N}{\sum\limits_{n = 1}^{N}{x\lbrack n\rbrack}}}}} & (2) \\{{\overset{\_}{x}\lbrack n\rbrack} = {{x\lbrack n\rbrack} - {\mu_{x}{\forall n}}}} & (3)\end{matrix}$

Applying the window function w[n] is described bya[n]=w[n](x[n]−μ _(x))∀n  (4)

The autocorrelation function for x is derived from r_(a)[T] and scaledelement by element of the autocorrelation function for the windowfunction r_(w)[T].

$\begin{matrix}{{r_{x}\lbrack\tau\rbrack} = \frac{r_{a}\lbrack\tau\rbrack}{r_{w}\lbrack\tau\rbrack}} & (5)\end{matrix}$

From non-overlapping blocks of samples from A/D module 20, a reliableblock processing scheme for arc detection can be achieved. Referring nowto FIGS. 3A and 3B, plots of normalized samples acquired at a rate of100 MSPS are shown. FIG. 3A shows a voltage envelope 50 and FIG. 3Bshows current envelope 60. An arc event occurs at approximately 150 μS,which is indicated at arrow 52 and ends approximately 23 μS later asindicated at arrow 54. By visual inspection, the transient behavior isdetectable in the voltage and current signals.

For M:=6 and N:=44, the autocorrelation function is computed for thevoltage and currents signals using a Hanning window of length N. Theautocorrelation for the voltage signal is shown in FIG. 4. FIG. 5 showsthe autocorrelation function for the current signal. Since the voltageand current signals contains the periodic component for the fundamentalfrequency (13.56 MHz in this case, however it should be appreciated thatother frequencies may be used) the corresponding correlation functionsare also periodic. Lags of τ also indicate periodic harmonic componentsemitted from the plasma. At time 52, the autocorrelation functionsproduce an abrupt change that coincides with the initial appearance ofthe arc event. After momentarily achieving a steady state value toindicate the arc is occurring, the autocorrelation functions againindicate a sharp transition that coincides with the end of the arc eventat time 54. FIG. 6 shows cross correlation function r_(vi)[T]; alsoproduces a periodic function with a visually detectable arc event.

The arc detection method should be invariant to different plasma loadimpedance and power levels. The cross correlation function of thevoltage and current signals provides immunity to a broader range ofsignals over an entire Smith Chart. The arc event is apparent betweentimes 52 and 56 (see FIG. 6) and detectable from the cross correlationfunction. Next is a description of how the function can be used for anarc event detector.

Analysis module 22 should keep false positive arc detections to aminimum. To achieve this, analysis module 22 includes probabilisticmodule 36. Probabilistic module 36 implements a probabilistic frameworkthat assigns likelihood to the number of arc events detected. Falsepositive could be attributed to normally occurring transients andinstabilities arising from a change in power levels or even more abrupt,the ignition of the plasma. The solution to the latter is employ enablesignal 28 to engage arc detection when the plasma is in a steady state.This is important in applications like pulsing, where the plasma statefollowing transition periods could otherwise be mistakenly detected asan arc event.

Correlation module 34 implements a first difference of the correlationfunction with respect to the j^(th) correlation function, r_(vi)^(j)-r_(vi) ^(j-1). In the first difference of the autocorrelationfunctions for the voltage (FIG. 9) and current signals (FIG. 10),prominent ridges are detectable for instances in time to detect thecommencement and extinguishment of the arc event. FIG. 11 shows a plotof the normalized output for the cross correlation of the voltage andcurrent. In this plot, the duration of the arc even is palpable with theduration time indicated.

Since the correlation function is even, an efficient implementation ofthe arc detection can be implemented by considering only half of thecorrelation function. Additionally, only the correlation functions forevery k^(th) block of N samples need to be processed. This scenario isexamined under a challenging condition where the arc event is in theorder of a few samples with low signal amplitude when compared to thearc event of FIGS. 3A and 3B. The data used for this case is shown inFIGS. 12A and 12B. FIG. 12A shows voltage envelope 50 and FIG. 12B showscurrent envelope 60. The arc is shown as descending spike at 70. Fork:=4, the corresponding arc detection, indicated with large spikeresponse at the arc event 70. The arc detection using the crosscorrelation function for voltage and current is shown at FIG. 13.

Referring now to FIG. 14, a first-order Markov chain is shown thatdescribes the probabilistic framework for a procedure to analyze thetransitions of an arc event. By sequentially aligning this model to anindex such as time, a trellis is created that spans a duration. Theduration could equate to the length of time for a process step, theentire process, or greater.

The Markov chain includes three states: no arc (S₀), arc event detected(S₁), and arc event occurring (S₂). Probabilities are also presented todescribe the transition probabilities P_(mn) from state m to state n.From the two examples used to describe our approach, we can ascertainhow this model reflects the detection of varying durations of arcs. Inthe first example of FIGS. 3A and 3B the arc event lasted approximately23 uS. For that case, the Markov chain would start at state S₀ andtransition to S₁ when the arc event was detected at time 52. Since thatexample had an arc event that remained active, the transition from S₁ toS₂ would be indicative of this scenario. During the arc event, the statewould remain in S₂ until the end of the arc event was detected at time54. At time 54 the state transition from S₂ to S₁ would occur andfinally S₁ to S₀ to indicate the extinguishment of the arc. Thesediscrete state transitions are described by a sequence of V:=[ . . . S₀S₀ S₁ S₂ S₂ . . . S₂ S₂ S₁ S₀].

Similarly, the sequence to describe the second arc event depicted inFIGS. 12A and 12B would be of V:=[S₀ S₀ S₁ S₁ S₀]. The transition to S₁₂does not occur there because of the short duration of the arc event.

The eloquence of using this framework is it provides the capability todetermine the likelihood of an arc event from which the system engineercan use this information to adjust the process parameters andquantitatively determine the resulting improvement. The decoding of thissequence, using observations from the arc detector, is accomplished byusing the Viterbi algorithm. This algorithm produces the probability ofthe observed sequence from

${{P(V)} = {\sum\limits_{\forall j}{{P\left( {V❘w_{j}} \right)}{P\left( w_{j} \right)}}}},$where w represents the vector of unobservable states in our model Itshould be appreciated that no limitation is placed on including otherobservable information such as RF and other process affectingparameters.

As a quantitative indicator to aid the system engineer with improvementsattributed to process adjustments, the state transition probabilitiescan also be computed. As adjustments are made and the process is run,the observable information is collected. Using this information with apost-process algorithm, the transition probabilities can be computed andcompared to the state transition probabilities prior to the adjustments.These probabilities are computed using an expectation maximizationalgorithm. The expectation maximization algorithm is an iterativealgorithm used to maximize the model parameters based on the observeddata. There are two steps to the expectation maximization algorithm. Inthe first step, the probabilities are marginalized given the currentmodel. For the first iteration, initial conditions are applied to themodel. During the second iteration the model parameters are optimized.This procedure iterates over these two steps until convergence of themodel parameters is achieved. The procedure is described by thepseudo-code of Table 1.

TABLE 1 1.) Initialize  model  parameters  and  obtain observation data2.) Until Convergence  a.   Compute  the  estimated  transition  probabilities α(j)  b.   Compute the estimated state probabilities  β(j)  c.   Update α(j+1) := α(j)  d.   Update β (j+1) := β (j) 3.) End

In some embodiments, analysis module 22 may be implemented in the analogdomain. In such embodiments A/D module 20 may be eliminated (see FIG. 1)and analysis module 22 receives analog first and second signals from RFsensor 16. Also, subtraction module 30, window module 32, the windowfunction, and correlation module 34 are also implemented in the analogdomain.

Those skilled in the art can now appreciate from the foregoingdescription that the broad teachings of the disclosure can beimplemented in a variety of forms. Therefore, while this disclosureincludes particular examples, the true scope of the disclosure shouldnot be so limited since other modifications will become apparent to theskilled practitioner upon a study of the drawings, the specification,and the following claims.

1. An arc detection system for a plasma generation system, comprising: aradio frequency (RF) sensor that generates first and second signalsbased on a respective electrical properties of (RF) power that is incommunication with a plasma chamber; and a correlation module thatgenerates an arc detect signal based on cross-correlating the first andsecond signals, wherein the arc detect signal indicates whether an arcis occurring in the plasma chamber and is employed to vary an aspect ofthe RF power to extinguish the arc.
 2. The arc detection system of claim1 further comprising a subtraction module that subtracts signal levelsfrom respective ones of the first and second signals.
 3. The arcdetection system of claim 1 further comprising a window module thatapplies a window function to the first and second signals.
 4. The arcdetection system of claim 1 further comprising a probabilistic modulethat computes a probability of an arc event based on the arc detectsignal.
 5. The arc detection system of claim 4 wherein the probabilisticmodule employs a Baum-Welch algorithm to calculate a probabilistic modelof the arc event.
 6. The arc detection system of claim 5 wherein theprobabilistic module employs a Viterbi algorithm to compute theprobability of the arc event.
 7. The arc detection system of claim 1wherein the correlation module receives an enable signal thatselectively enables generating the arc detect signal.
 8. The arcdetection system of claim 1 wherein further comprising ananalog-to-digital (A/D) conversion module that digitizes the first andsecond signals.
 9. The arc detection system of claim 1 wherein the RFsensor is a voltage/current (V/I) sensor and the first and secondsignals represent a voltage and current, respectively, of the RF power.10. The arc detection system of claim 1 wherein the RF sensor is adirectional coupler and the first and second signals represent theforward power and reflected power, respectively, of the RF power.
 11. Anarc detection method for a plasma generation system, comprising:generating first and second signals based on a respective electricalproperties of (RF) power that is in communication with a plasma chamber;and generating an arc detect signal based on a cross-correlation of thefirst and second signals, wherein the arc detect signal indicateswhether an arc is occurring in the plasma chamber; and employing the arcdetect signal to vary an aspect of the RF power to extinguish the arc.12. The arc detection method of claim 11 further comprising subtractingsignal levels from respective ones of the first and second signals. 13.The arc detection method of claim 11 further comprising applying awindow function to the first and second signals.
 14. The arc detectionmethod of claim 11 further comprising computing a probability of an arcevent based on the arc detect signal.
 15. The arc detection method ofclaim 14 wherein the computing step further comprises employing aBaum-Welch algorithm to calculate a probabilistic model of the arcevent.
 16. The arc detection method of claim 15 wherein the computingstep further comprises employing a Viterbi algorithm to compute theprobability of the arc event.
 17. The arc detection method of claim 11further comprising receiving an enable signal that selectively enablesgenerating the arc detect signal.
 18. The arc detection method of claim11 further comprising digitizing the first and second signals.
 19. Anarc detection system for a plasma generation system, comprising: a radiofrequency (RF) sensor that generates first and second signals based on arespective electric properties RF power that is in communication with aplasma chamber; an analog-to-digital (A/D) conversion module thatgenerates digital data based on the first and second signals; asubtraction module that subtracts values from the digital data; a windowmodule that applies a window function to the digital data; a correlationmodule that cross-correlates the first and second signals as they arerepresented in the windowed digital data and that generates an arcdetect signal based on the correlation, wherein the arc detect signalindicates whether an arc is occurring in the plasma chamber.
 20. The arcdetection system of claim 19 further comprising a probabilistic modulethat computes a probability of an arc event based on the arc detectsignal.
 21. The arc detection system of claim 20 wherein theprobabilistic module employs a Baum-Welch algorithm to calculate aprobabilistic model of the arc event.
 22. The arc detection system ofclaim 21 wherein the probabilistic module employs a Viterbi algorithm tocompute the probability of the arc event.
 23. The arc detection systemof claim 19 wherein the correlation module receives an enable signalthat selectively enables generating the arc detect signal.
 24. The arcdetection system of claim 19 wherein the RF sensor is a voltage/current(V/I) sensor and the first and second signals represent a voltage and acurrent, respectively, of the RF power.
 25. The arc detection system ofclaim 19 wherein the RF sensor is a directional coupler and the firstand second signals represent a forward power and a reflected power,respectively, of the RF power.